Experimental and numerical analysis of pGFRP and wood cross-arm in latticed tower: a comprehensive study of mechanical deformation and flexural creep by Amir Abd Latif, Mohamad Ridzwan Ishak, Muhammad Rizal Razman, Noorfaizal Yidris, Mohamed Yusoff Mohd Zuhri, Muhammad Asyraf Muhammad Rizal, Zuliskandar Ramli

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Risk of myocardial infarction and heart failure in gout patients: a systematic review and meta-analysis by Panpan Wang, Huanhuan Yang

“…Relevant data were extracted from the final screened literature, and a forest map was drawn using RevMan 5.3 software for meta-analysis. …”
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Inconspicuous taxa in citizen science-based botanical research: actual contribution, limitations, and new opportunities for non-vascular cryptogams by Carlos Cerrejón, Carlos Cerrejón, Marion Noualhaguet, Nicole J. Fenton, Marc-Frédéric Indorf, Mariano J. Feldman, Mariano J. Feldman

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Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker by Ni M, Peng W, Wang X, Li J

“…Next, core module genes were screened using WGCNA and then the hub genes were characterized using LASSO and random forest methods. Besides, the associations between hub genes, immune cells, and key pathways were explored. …”
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The role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis by Gujun Cong, Jingjing Shao, Feng Xiao, Haixia Zhu, Peipei Kang

“…The XGBoost and Random Forest algorithms highlighted the significant role of COL1A1, and we further analyzed the expression and correlation of COL1A1, AR, and EGFR through multiplex immunofluorescence staining. …”
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Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women by Chen A, Chang X, Bian X, Zhang F, Ma S, Chen X

“…In the training set, Random Forest (RF) algorithm, backpropagation neural network (BPNN) and Logistic Regression (LR) were used to construct a MACE risk prediction model for perimenopausal women, and the test set was used to verify the model. …”
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Capacidad fitorremediadora de Ricinus communis asociado a cepas bacterianas de suelos agrícolas cacaoteros contaminados con cadmio by Merli Clarita Peña Zarate, Jean Carlos Silva Alvarez, Karol Noemi Vilchez Estrada, Natacha Razanadrasina, Nestor Valladolid, Gerardo J. F. Cruz, Eric Mialhe, Benoit Diringer

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Boron adsorption in semiarid Mediterranean soils under the influence of background electrolytes by S. Fatnassi, M.B. Almendro Candel, J. Navarro Pedreño, I. Gómez Lucas, M. Hachicha

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Pooled prevalence and associated factors of traditional uvulectom among children in Africa: A systematic review and meta-analysis. by Solomon Demis Kebede, Kindu Agmas, Demewoz Kefale, Amare Kassaw, Tigabu Munye Aytenew

“…Heterogeneity among the included studies was assessed using a forest plot, I2 statistics, and Egger's test, ensuring the robustness and reliability of the findings. …”
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Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia by Carl Bethuel, Damien Arvor, Thomas Corpetti, Julia Hélie, Adrià Descals, David Gaveau, Cécile Chéron-Bessou, Jérémie Gignoux, Samuel Corgne

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EVALUATION OF THE ADAPTIVE PROPERTIES OF SPRING BARLEY VARIETIES ACCORDING TO THEIR YIELD CAPACITY IN THE ENVIRONMENTS OF THE NEAR-IRTYSH AREA IN OMSK PROVINCE by P. N. Nikolaev, P. V. Popolzukhin, N. I. Anisimov, O. A. Yusova, I. V. Safonova

“…The experimental part of the work was  carried out during 2011-2017, on the experimental fields of the  Siberian Research Institute of Agriculture, RAAS, located in the  southern forest-steppe in the vicinity of Omsk. The plot area was 10  m2, with 4 repetitions. …”
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Floristic Diversity and Potential of Site of Biological and Ecological Interest Brikcha, Prérif (North-West Morocco) by Ahmed Chriqui, Ouafae Benkhnigue, Issam El Khadir, Yassine Mouniane, Sidi Mohamed Hachimi Alaoui, Lahcen Zidane, Driss Hmouni

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Impacto sobre el cambio climático del ciclo de vida de las tarimas de madera elaboradas en la región Huetar Norte de Costa Rica by John Solano Salmerón, William Fonseca González, Sebastián Ugalde Alfaro, Federico Alice Guier

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Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study by Yunzhen Ye, Yu Xiong, Qiongjie Zhou, Jiangnan Wu, Xiaotian Li, Xirong Xiao

“…Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. …”
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The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review by Ricardo Smits Serena, Florian Hinterwimmer, Rainer Burgkart, Rudiger von Eisenhart-Rothe, Daniel Rueckert

“…Furthermore, our analysis reveals the current dominance of machine learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for deep learning models in this area. …”
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Impact of #PCOSweightloss: A global X hashtag analysis study of weight loss narratives in the PCOS community by Munaza Afaq, Kumar Vashisht, Areen Al-Dhoon, Divya Abraham, Atanas G. Atanasov, Maima Matin, Zara Arshad, Rahul Kashyap, Faisal A. Nawaz

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Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach by Tarawneh AS, Al Omari AK, Al-khlifeh EM, Tarawneh FS, Alghamdi M, Alrowaily MA, Alkhazi IS, Hassanat AB

“…In addition, we experimented with the dataset’s missing values using the histogram gradient boosting (HGB) model.Results: The feature ranking method demonstrated the ability to distinguish cancer patients from healthy individuals based on hematological features such as WBCs, red blood cell (RBC) counts, and platelet (PLT) counts, in addition to age and creatinine level. The random forest (RF) classifier, followed by linear discriminant analysis (LDA) and support vector machine (SVM), achieved the highest prediction accuracy (ranging from 0.69 to 0.72 depending on the scenario and method investigated), reliably distinguishing between malignant and benign conditions. …”
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To Develop Biomarkers for Diabetic Nephropathy Based on Genes Related to Fibrosis and Propionate Metabolism and Their Functional Validation by Sha Li, Jingshan Chen, Wenjing Zhou, Yonglan Liu, Di Zhang, Qian Yang, Yuerong Feng, Chunli Cha, Li Li, Guoyong He, Jun Li

“…Second, the intersection of DN-DEGs, PM-DEGs, and FRGs was taken to yield intersected genes. Random forest (RF) and recursive feature elimination (RFE) analyses of the intersected genes were performed to sift out biomarkers. …”
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The probability of detecting host-specific microbial source tracking markers in surface waters was strongly associated with method and season by Claire M. Murphy, Daniel L. Weller, Tanzy M. T. Love, Michelle D. Danyluk, Laura K. Strawn

“…Variance partitioning analysis was used to quantify the variance in host-specific MST marker detection attributable to non-methodological and methodological factors. Conditional forest and regression analysis were utilized to assess the association between detection and select non-methodological and methodological factors. …”
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Variabilidad intraespecífica en la calidad de semillas de especies forestales nativas en bosques montanos en el sur del Ecuador: Implicaciones para la restauración de bosques... by Ximena Palomeque, Andrea Maza, Juan Pablo Iñamagua, Sven Günter, Patrick Hildebrandt, Michael Weber, Bernd Stimm

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